System and method for soil characterization

ABSTRACT

A system and method for characterizing matter, for example, soil organic content is disclosed. A radiation and electric field sensor measure sample properties before, during and after irradiation. Calibrations are developed relating those measurements to useful properties of matter, for example, soil density and organic content. As an example of an embodiment of the disclosed invention an instrument attachment for portable X-ray fluorescence instrumentation was prototyped enabling concurrent volumetric soil organic matter quantification. This primary prototype outperformed more expensive emerging visible-near infrared multivariate instrumentation using parsimonious soil specific simple linear regression (R2 ranged 0.85-0.97) enabling rapid, parallel, nondestructive, cost-effective acquisition of soil elemental concentrations together with organic content data.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority to United States provisionalpatent application Nos. 62/900,656, filed on Sep. 16, 2019, 62/891,353,filed on Aug. 25, 2019, and 62/805,315, filed on Feb. 14, 2019,disclosures of which are incorporated herein at least by reference.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

Not Applicable

THE NAMES OF THE PARTIES TO A JOINT RESEARCH AGREEMENT

Not Applicable

STATEMENT REGARDING PRIOR DISCLOSURES BY THE INVENTOR OR A JOINTINVENTOR

Not Applicable

FIELD OF THE INVENTION

The present invention is in the field of matter chemical and/or propertycharacterization.

BACKGROUND OF THE INVENTION

Development of innovative and disruptive environmental technologies areneeded to address the ever-increasing evolving environmental threats theEarth and its inhabitants face such as climate change. The UnitedNations Framework Convention on Climate Change (UNFCCC) recentlyintroduced the 4-per-1000 (4p1000) initiative in the Paris Agreements atthe 21st Conference of Parties (COP21 Nov. 2015); 179 of the 197 membercountry signatories have ratified the agreement and it came into forceas of November-2016 (UNFCCC, 2016). The 4p1000 initiative aims tomitigate climate change by increasing agricultural soil organic carbon(SOC) by 0.4% per year thereby reducing atmospheric CO2 (van Groenigenet al., 2017). As corollary, some countries are implementing ormodifying legislative framework incentivizing carbon farming byproviding subsidies or value for verified increases in SOC, makingcarbon farming potentially lucrative for farmers and landowners. Forexample, in Australia the “Carbon Credits-Carbon FarmingInitiative-F2018L00089” came into force January-2018 (Australian FederalGovernment—Australia, 2018). The Australia New Zealand Banking Group hasalso announced a carbon trading desk to accommodate the young carbonmarkets and allows consumers or corporate entities to sell earnedcredits, purchase credits to offset carbon emissions, or trade creditsas investment options (Australia New Zealand Banking Group—ANZ, 2018).Carbon farming is doubly advantageous for farmers because they can reapgovernmental incentives while increasing their soil quality andultimately, product yields (Rumpel et al., 2018). Economically measuringand monitoring soil characteristics such as SOC is critical in all ofthese initiatives, and advances in rapid assessment instrumentation andtechniques are needed to address evolving global policy, climate changemitigation and monitoring needs. For example, climate scientists,environmental scientists and agriculturalists require quantification ofSOM and SOC because of the critical influence on many factors includingpollution risk, nutrient bioavailability, agricultural productivity andclimate change (DAFWA, 2013; Lal, 2006; Reeves, 1997; van Groenigen etal., 2017).

Soil properties such as soil organic matter (OM) and SOC are typicallyquantified using destructive wet oxidation, loss on ignition (LOI) ordry combustion (DC) techniques which are time consuming, costly, orproduce environmentally harmful byproducts (Assunção et al., 2019;Rumpel et al., 2018; Soriano-Disla et al., 2014; Wang et al., 2015). Inscience and statistics simplicity and parsimoniousness is desired frommodels (Bentler and Mooijaart, 1989), i.e. the least complex model thatsufficiently describes observed phenomenon is desired. Emergingmultivariate spectroscopy methods and instruments such as visible nearinfrared (Vis-NIR), mid-infrared (MIR), or Fourier transform infrared(FTIR) devices have been used to characterize SOC (Assunção et al.,2019; Rumpel et al., 2018; Soriano-Disla et al., 2014; Wang et al.,2015). Such chemometric and multivariate reliant instrumentation developmodels that are less statistically parsimonious compared to simplelinear regression (SLR) models because multivariate methods utilizemultiple predictors whereas SLR utilizes a single independent variable.These spectroscopy techniques are also limited to sample surfaces and donot analyze sample volumetric composition. Portable X-ray fluorescence(XRF) is another example of a rapid, mobile, non-destructive, highthroughput and economical device well suited for investigations wheremany samples must be analyzed and/or dense characterization of withinsite spatial variability in geochemical composition is needed (Rouillonand Taylor, 2016; Rouillon et al., 2017; Taylor et al., 2004). PortableXRF can be employed both in-situ or ex-situ and possesses the advantageof producing measurements based on elementally and matrix dependentvolumes of analyses (Ravansari and Lemke, 2018). Portable XRF deviceshowever cannot quantify light elements which are the main constituentsof SOM and SOC (e.g. carbon, oxygen), and thus they cannot be used todirectly quantify these parameters via parsimonious simple linearregression (Ravansari and Lemke, 2018). Attempts at combining PXRF andVis-NIR (e.g. United States patent U.S. Ser. No. 15/319,816) dataincrease predictive power (Wang et al., 2015) of multivariate methodsbut move away from cost-efficiency and simplification. Adapting PXRFdevices at low cost to generate information such as SOM or SOC inaddition to other information so would significantly advance capacityand meet the needs of parties with a vested interest in soilcharacterization (e.g. farmers or soil scientists).

Therefore, disruptive environmental innovation is needed to decrease thecost of environmental monitoring and sample characterization to addressthe increasingly complex problems humanity faces (Sedlak, 2018). Thispatent details the advancement of existing PXRF technology using novelsensor integrations which enable parallel auxiliary data acquisitionduring regular analyses. This additional data can be used to bettercharacterize soil chemical characteristics than what is possible withPXRF alone.

SUMMARY OF THE INVENTION

The present invention discloses novel means of obtaining additionalsample information in parallel to portable X-ray fluorescencemeasurements. Such information is obtained by positioning an electricfield sensor near or within a sample undergoing radiation bombardment(via intrusive electrodes, contactless electrodes, electric fieldsensors, or otherwise). The electric field is measured before (toestablish a baseline), during (to observe variation over time) and afterirradiation ceases (to measure the final electric field and thesubsequent relaxation and attenuation of the sample's electric fieldover time). These electric field measurements are calibrated to one ormore of the sample's properties such as soil organic content. Differentatoms possess different fluorescence and electron yields with lighterelements possessing very high electron yields. There exists an inverserelationship between fluorescence yield and electron yield for elementalK-alpha emissions which is one of the reasons it is difficult to obtaina good signal for light elements using the X-ray fluorescence method(light element fluorescence yields are low and electron yields arehigh). The electric properties of a sample may vary depending on thesample's composition as electrons are excited or knocked off the sampleduring radiation bombardment (which may generate an electric field,voltage or potential). This information is indicative of a sample'sproperties such as but not limited to soil organic content.

The present invention discloses a novel portable X-ray fluorescenceinstrument no attachment to further obtain additional sample informationin parallel to portable X-ray fluorescence analyses by positioning aradiation sensor adjacent to a sample undergoing radiation bombardment.The radiation escaping the sample undergoing bombardment is measured andrelated to one or more properties of the sample such as density ororganic content.

The present invention also discloses a novel means of disturbing orstandardizing a non-solid sample such as soil or water by utilizing asurface transducer to send vibrational energy through a sample.

The present invention also discloses a novel calibration method wherebymore accurate results can be achieved using multivariate or neuralnetwork models by predicting correction coefficients to be applied tomeasurements obtained using simpler methods (as opposed to predictinganalytes directly using such multivariate and neural network models).

In an embodiment, for ex-situ analyses samples are obtained from thefield and bagged and tagged with an NFC sticker that has data such asGPS, date, time, sampler name, or other data written to it using adevice such as a mobile phone. The sample can then be brought back to alab, processed and analyzed with the data being written or appendeddirectly to the NFC chip in addition to potential upload to a networkconnected database. This has the advantage that the data is directlyaccessible to any NFC enabled device, either connected to a network ornot connected to the network since information is directly available onthe NFC tag. A device such as a phone, mobile application is used totransfer GPS, date, time and instrumental data directly to an NFC tagfor field applications. The data is directly accessible to any NFCenabled device, either connected to a network or not connected to thenetwork since the data is directly available on the NFC tag. Forexample, this is advantageous as opposed to having to associate a uniqueidentifier with the relevant data. Instead, relevant data such as GPSlocational information is directly stored on the sample itself via anadhesive or attached NFC tag on the sample or sample container which isaccessible via any NFC reader device regardless of whether the reader isusing a unique identifier to associate data to the sample, pulling orpushing data to or from a database via a network connection. Theessential information is stored on the sample or sample container itselfwhich is highly advantageous.

In another embodiment, the electric field of the sample is measuredbefore, during and after bombardment by radiation using one or moreintrusive electrodes which measure potential. They can be locatedadjacent to the area of irradiation such that the irradiating beamilluminates the sample and one or more intrusive electrodes are locatedon one or both sides of the irradiated area enabling measurement of thepotential between these electrodes or if a single electrode is used, thepotential with respect to some other reference, for example, voltagebetween the sample and ground.

In another embodiment, ambient environmental sensors (e.g. humidity,temperature, barometric pressure) are to be incorporated to loginformation which can be used by algorithms to account or correct forvariability in electric field buildup and dissipation characteristics ofthe sample. For example, an electric field measurement conducted on arelatively humid day will lead to a faster dissipation of the chargebuild up and may affect measurements on the sample during and afterbombardment leading to variability in measurement of the sample electricproperties conducted on other days of variable humidity.

An embodiment of the present invention comprises a modular, clip on orattachable PXRF instrument attachment comprised of one or more of thefollowing: a radiation detector, electric field sensor, transducer, NFCreader and/or writer.

An embodiment of the present invention comprises one or more of thefollowing: a radiation source, electric field sensor, radiationdetector, transducer, NFC reader and/or writer.

An embodiment of the present invention involves a version for in-situapplications comprising an intrusive radiation detector and electricfield sensor that penetrates the soil profile.

In another embodiment, foregoing the PXRF and its associated data, astandalone system is used comprising a pyroelectric radiation generator,tube, or means of radiation production in conjunction with an electricfield sensor and/or a radiation detector for obtaining information onone or more sample matter properties or characteristics. The radiationsource's flux output can be monitored using a secondary radiationdetector placed near the source's output and corrections applied toaccount for flux variability in instances where radiation flux may beinconsistent such as with pyroelectric X-ray generators.

In another embodiment, a core scanner is employed. The platform on whichthe sample (core) lays is larger to accommodate a soil core and theplatform or components such as the X-ray generator, electric fieldsensor, or radiation sensor move relative to one another so thatdifferent areas of the core can be scanned with the invention (with theplacement of a radiation detector or electric field detector adjacent tothe core undergoing analysis).

In another embodiment, an insulated and ungrounded PXRF stand isemployed. The PXRF stand or bench is insulated by means of a base whichis elevated off the surface on which the PXRF rests using insulatingpedestals. The device will allow for a longer lasting charge to build onthe sample for better detection of the electric field sensor. Thus, inbenchtop mode, it is useful in certain situations to electricallyinsulate the PXRF bench by not using grounded components, for exampleutilizing the PXRF on battery power or using special wires which willnot ground the PXRF or bench via wires connected to ground. For example,wires connected to a laptop or PC are typically grounded when plugged inand use of such wires may be less advantageous that using special wiresthat do not complete a connection to ground.

In another embodiment, the analysis platform is comprised of entirelyinsulating material to allow better electric field buildup anddetection. In the case where dangerous radiation is employed, theshielding will wrap around the components comprising the inventionincluding the insulating material because insulating material may not besufficient for shielding requirements. The shielding enclosure in whichthe sample rests during analyses can be covered either internally orboth internally and externally with a layer of insulating material.

In another embodiment, a digital or manual grounding mechanism forgrounding the sample and ungrounding the sample exists to reset thecharge on the sample for successive repeated measurement. This willallow repeated electric field measurements to be taken on the sample.

In another embodiment, the transducer is in contact with the sampleeither directly or indirectly via a transducing medium and it turns onprior to, during or after analysis. This is done to disturb the sampleand change the immediate matter within the analysis area of the samplebeing analyzed (to obtain more representative measurements or othercharacteristics of the sample for example, signals from different sizefractions of particulate matter such as soil, since grain sizefractionation may occur during vibration). This can also be done tostandardize the sample's density and compaction characteristics prior toanalysis for measuring matter characteristics such as soil organiccontent as described.

In another embodiment, a diffraction crystal is used to control incidentenergy of radiation bombardment for measuring sample electric propertiesbefore, during and after radiation bombardment. The diffraction crystalmay be rotated to bombard the sample with different energies andmeasuring penetrating radiation or sample electric properties which maybe related to one or more properties of matter such as soil organiccontent, for example. The electrons in atoms are excited by differentenergies and thus controlling the bombardment energy of incidentradiation using for example, a diffraction crystal may affect thesample's electric properties in a way that is indicative of one or moresample properties such as composition.

BRIEF DESCRIPTION OF THE DRAWINGS

A variety of types and shapes of electric field sensors, radiationdetectors and radiation sources, and transducers exists, therefore thetypes, shapes and relative sizes of these components are not limited tothose depicted in the following figures. Furthermore, the geometries,assemblies and relative positioning of these depicted components withrespect to one another are not limited to those portrayed in these FIGS.because detection of the relevant signals can be achieved using manydifferent positional configurations.

Lexicographic note: The figures depicted in the drawings with pagenumbers starting with “Study” are associated with the study which wasconducted and described in the detailed descriptions section of thisdisclosure. These figures are referenced by figure captions within thedetailed description section. Drawings whose page numbers do not startwith “Study” are captioned here and further referenced and described inthe detailed descriptions.

FIG. 1A an angled view of an embodiment of the PXRF instrumentattachment.

FIG. 1B is side view of an embodiment of the PXRF instrument attachment.

FIG. 1C is another view an embodiment of the PXRF instrument attachment.

FIG. 2 is an embodiment for the frame containing the radiation detectorand/or the electric field sensor.

FIG. 3 is an embodiment for the frame comprising the invention whichcontains an analysis container.

FIG. 4 is an embodiment for the transducer system described herein.

FIG. 5 is an embodiment for the transducer system which is in contactwith the analysis vial.

FIG. 6A is an embodiment for an analysis container.

FIG. 6B is an embodiment for an analysis container that is fitted snuglyinto a groove on the frame for consistency during analyses.

FIG. 7A is another embodiment for an analysis container.

FIG. 7B is another embodiment for an analysis container which is fittedsnugly into a groove on the frame of the analysis platform forconsistency during analyses.

FIG. 8 is an embodiment for an ex-situ version of the invention wherecomprising intrusive detector.

FIG. 9 is an embodiment of the intrusive electrodes placed adjacent toan irradiating beam for electric field measurement before, during andafter radiation bombardment.

FIG. 10 is an embodiment of a method for calibrating measurements.

FIG. 11 depicts the residual electric discharge fractal pattern on ananalysis vial which was bombarded by radiation during PXRF analyses. Itis requested that this image be retained in color because the intricatefractal electric discharge pattern depicted on this photo cannot beappreciated in a black and white image.

DETAILED DESCRIPTION

The present invention is described in enabling detail in the followingexamples, which may represent more than one embodiment of the presentinvention. Terms such as “a”, “the”, and “an” may not refer to a singlearticle but rather the general type of article to which the referencedarticle belongs.

FIG. 1A 101 is one example of the analysis vial which is analyzed by thePXRF. 102 can comprise the contactless electric field sensor and/orradiation detector. FIG. 1B can be a side view of the invention. FIG. 1Cdepicts another view of an example of the invention. 103 depicts anembodiment of the X-ray bombardment and detection of PXRF devices butthese example shapes, geometries relative positions are not limiting.

FIG. 2 is one example of a PXRF instrument attachment frame comprising201 which can be the radiation detector and/or the electric fielddetector. The groove in the frame 202 fits analysis containers so thatthe distance between the electric or radiation detectors and the sampleundergoing analysis are consistent between all analyses.

FIG. 3 is one example of a PXRF instrument attachment frame 303 which isloaded with a container to be analyzed by the electric and/or radiationsensor(s).

FIG. 4 is one example of an embodiment for the transducer system whichis comprised of a transducer 403, the surface of the transducer 402 anda grove into which an analysis container can be placed for subsequentvibration of the sample. The transducer surface 402 can also be put incontact with the sample indirectly via a transducing medium, such as thePXRF instrument attachment frame, for example.

FIG. 5 is one example of an embodiment for the transducer system'sincorporation with the frame 505 comprised of the radiation detectorand/or electric field detector 504. The transducer 501 is put intocontact with the analysis container 503 and at some point before, duringor after bombardment by radiation, vibrational energy is produced by thetransducer 501 and transferred to the analysis container 503.

FIG. 6A is an example of an analysis vial comprising of a non-permeablesidewall capable of containing for example, liquid or particulate mattersuch as water or soil.

FIG. 6B is an example of an analysis vial which fits snugly into a frame604 to control distance and positioning of the analysis container 601with respect to the electric and/or radiation detector(s) 603. Theanalysis container 601 possesses one or more thin but strongnon-permeable sidewalls 602.

FIG. 7A is another example with a varying geometry of an analysis vialcomprising of a non-permeable sidewall capable of containing forexample, liquid or particulate matter such as water or soil.

FIG. 7B is an example of an analysis vial which fits snugly into a frame704 to control distance and positioning of the analysis container 701with respect to the electric and/or radiation detector(s) 703. Theanalysis container 701 possesses one or more thin but strongnon-permeable sidewalls 702.

FIG. 8 is an example of an embodiment of the invention as an in-situsoil specific PXRF device in which example embodiments may beimplemented. Pilot holes consisting of the exact dimensions of theintrusive enclosure 807 will be carved out of the soil 802 sampling spotby hammering hollow tube into the soil, this will prevent wear and tearon the protruding enclosure 807 by generating a hole into which theprotruding X-Ray sensor enclosure 807 can fit into. The X-Ray source 801tracks the sensor's 806 movement and moves with it to shoot irradiatethe soil profile and shoot an X-ray beam 804 into the soil profile atthe surface normal to the soil profile with the intrusive sensor 806located adjacent to the X-Ray beam 804. It may also be possible to shootthe X-Ray beam 804 diagonally towards the detector for measurement ofX-Ray attenuation properties of the soil. The sensor 806 itself can bestationary or it can move within the enclosure 807 enabling theacquisition of soil information including but not limited to soildensity, and organic matter via attenuation measurements. It will alsoenable the acquisition of analyte concentrations at different depthswithin the soil profile thus mitigating problems arising from analytevertical stratification (because the sensor 806 can move within itsenclosure 807). The integrated water and temperature sensor 803rendering volumetric water content coupled with density determinationsare useful and may enable seamless calculation of one or more soilproperties including gravimetric water content by the onboardelectronics 805. The gravimetric water content will be used toseamlessly correct in-situ measurements for deviations caused by watereffects via onboard computers and algorithm. Components of deviations inmeasurement accuracy caused by water effects and organic matter arefurther dealt with using the calibration method described herein. Thesemethods used in concert enable total hydrofluoric digestion ICP qualitydata acquisition right in the field. Intrusive electrodes (not shown)will also penetrate the soil and they are located adjacent to the X-Raybeam such that the X-Ray beam is located in the center and intrusiveelectrodes are located on both sides of the X-Ray beam. This is done tomeasure soil electric properties before, during and after X-Raybombardment. For light elements auger electron generation dominates whensamples are bombarded by X-Rays. Thus, measurement of soil electricproperties in samples before, during and after X-Ray bombardment viaintrusive electrodes enables extraction of soil chemical information.Orchestration and integration of these sensors enable soil chemicalcharacterization in a new novel and more effective manner than what iscurrently possible.

The embodiment of FIG. 8 may operate as follows:

a) Control and processing computer 805 initiates the emission ofradiation from the radiation source.

b) The radiation source 801 irradiates the sample.

c) The radiation sensor 806 and other sensors (e.g., electric fieldsensor, X-ray sensor attached to the device as described above) detectthe radiation traversing the sample or variations in soil propertiesbefore, during and after X-Ray bombardment.

d) During X-Ray bombardment, the X-Ray sensor situated within theenclosure moves to obtain soil chemical information from different areasof the soil sample undergoing analysis (vertical stratificationinformation).

e) The sensors utilize the CPU 805 to log the information generated byall sensors to the memory.

f) The GPS generates spatial information associated with where thesample was analyzed.

g) The CPU 805 logs the information from the sensors and GPS to computermemory.

h) The CPU 805 uses the information obtained from the sensors and GPS torender useful soil information such as but not limited to soil watercontent, soil organic content, density, analyte concentrations,stratified analyte concentrations, and/or signals from different areasof the sample.

i) The information is logged to computer memory for future use and/ortransferred to an online database or a computer database via wireless ora wired connection.

j) The rendered information is used to generate accurate XRF geochemicalmeasurements by accounting for variability in matrix compositions viaonboard CPU and/or computer algorithms or for other purposes.

FIG. 9 is an example of the invention where intrusive electrodespenetrate a sample, for example, a soil profile 901, and the electricproperties between the two electrodes 904 and 902 are measured before,during and after bombardment by radiation 903.

FIG. 10 is an example of the calibration method 1003 which may beemployed if multidimensional data 1001 and some other analytemeasurement 1002 is available. Correction coefficients may be computed1004 using the available analyte concentration 1002 and predictivemodels developed for prediction of correction coefficients usingmultidimensional data 1005. These models can then be used on unknownsamples to apply corrections to measurements rendered using othercalibration models 1006.

FIG. 11 depicts the residual electric discharge fractal pattern 1101 onan analysis vial which was bombarded by radiation during PXRF analyses.Sample electric properties may change during bombardment therefore,electric properties may be detected before, during and after radiationbombardment and related to a sample's properties such as organiccontent, for example. The detected electric field may thus be indicativeof a sample's properties such as soil organic content for example.

A non-limiting example of a study investigating some aspects of thepresent invention is now described. It will be apparent to the skilledartisan that the present invention may have other uses, for example, theanalysis of other types of environmental samples such as geologic mediaor water or for the characterization of other properties of matter apartfrom the specific examples discussed here which are soil organic contentand density. The descriptions of the relative positioning of thecomponents comprising the present invention with respect to one anotherare non-limiting and are examples of specific embodiments. The settings,features and components used in this example study are non-limiting(such as analysis times, component operation settings, types of sensors,detectors, instruments, tube voltage, current, analysis times, andother).

Soil organic matter and organic carbon are variables of criticalenvironmental importance in terms of soil productivity, global foodsecurity, and climate change mitigation. Rapid and accurate assessmentof these variables is central to national programs and internationalagreements. Portable X-ray fluorescence instruments are widely used torapidly quantify and map soil elements, however quantification of lightelements comprising organic content is not yet possible. We developed anovel attachment for portable X-ray fluorescence instrumentationenabling concurrent volumetric soil organic matter quantification. Thisprimary prototype outperformed more expensive emerging visible-nearinfrared multivariate instrumentation using parsimonious soil specificsimple linear regression (R2 ranged 0.85-0.97) enabling rapid, parallel,nondestructive, cost-effective acquisition of soil elementalconcentrations together with organic content data.

Theoretically, critical X-ray penetration depth is a function of asample's bulk density (BD) and mass attenuation coefficient (Parsons etal., 2013; Potts and West, 2008). For soils, both BD and attenuationcoefficient typically decrease as organic matter increases (Adams, 1973;Saini, 1966), therefore volumetric X-ray penetration is potentiallyconfounded by both of these factors and an assessment of the importanceof each was required to investigate the relationship between X-raypenetration and soil organic content. Data quality may also increasewhen the interplay and influence of these factors are accounted for incalibrations. Based on these principles, we developed an instrumentattachment for PXRF which measures SOM by relating soil X-raypenetration to organic matter content. A Tracer III-SD PXRF(Bruker-United States of America) device was operated in benchtop modeand was fitted with a platform on which a Type V (Radiation Watch-Japan)radiation sensor was mounted. The sensor's photodiode was a X-100-7 100mm2 PIN detector (First Sensor-Germany). The sensor was placedorthogonal to the PXRF analyzer surface area and adjacent to wheresamples are placed for analyses as shown in FIG. 1 (henceforth referredto as the Z-Plane sensor). We refer to the “Z-Plane sensor” as suchbecause we have defined the PXRF analysis platform surface area asexisting in the x and y plane and the sensor is situated orthogonally inthe z-plane such that it captures otherwise egressing X-rays that escapethrough the analysis vial (FIG. 1). The detection of these X-rays at theZ-Plane sensor were indicative of a sample's organic content.

The PXRF was operated at 40 kV, 10 μA and samples were analyzed for 3minutes each. Two soil types (Vertisol, Cambisol) (IUSS Working Group,2006) and an unconsolidated sand were prepared by drying, grinding andsieving to the <250 μm fraction. The soils were then loaded with varyingamounts of powdered and sieved (<250 μm) organic matter surrogates(Lucerne and sucrose). Samples were spiked with either Lucerne orsucrose to provide a range of SOM values from 0 to approximately 20%. Anatural Ferrosol soil was also used to assess the instrument'sperformance and was not subjected to these preparation methods but wasinstead sampled from the field and prepared according to standardpractices (SM) for analysis by LECO combustion, PXRF and the new Z-Planeinstrument. Three different trials were conducted, trial 1 employed thetypical method for PXRF analyses where sample vials are filled to apredetermined depth (1.5 cm in our case) and analyzed without furthertreatment. Trial 2 utilized a method where BD and depth of samples werecontrolled and characterized via pre-analysis compaction to assesseffects on measurements (SM). Trial 3 utilized the same method as trial1 with the exception that compaction was regulated using a transducer(SM). Trial 2 and trial 3 samples were also analyzed using a TerraSpecVis-NIR device (ASD Inc.-United States of America). Various combinationsof the multidimensional PXRF, Vis-NIR and single dimensional Z-Planedata were integrated and modeled using partial least squares regression(PLSR) or simple linear regression (SLR) and model performances wereevaluated for different combinations of the data (SM).

Evaluated using United States Environmental Protection Agency (USEPA)soil data quality criteria (USEPA, 1998), results were on averagequantitative for trial 1. Average coefficients of determination (R2)were high and ranged between 0.92-0.93 and average relative standarddeviations ranged between 19.64%-27.49%. For trial 2, a post compactionsample depth>1.2 cm was empirically determined not to affect Z-Planemeasurements and all samples were verified with a sample depth greaterthan this threshold (SM). Significance assessment P value cutoffs wereadjusted to 0.01 from the typical 0.05 utilized in soil sciences toaccount for the multiple comparisons drawn across differentsoil/surrogate combinations. Experimental repeatability was high (allreplicate regression comparison P values 0.23) (SM). There was aconsistent soil type effect across both trials (all P values<0.01) withhigher Z-Plane counts associated with the lighter textured Cambisol andthe sand as compared with the heavy clay textured Vertisol (SM). Organicmatter surrogate did not show a significant effect on sensor responsewhen BD was not controlled [trial 1] (all P values>0.01); however,surrogate type did exert a significant effect on detected responses whenBD was controlled [trial 2] (all P values<0.01) (SM). Sensor drift wasminimal throughout the experiments (SM). Analytical repeatability inmeasurements was high for triplicate analysis conducted on an adoptedstandard without re-homogenization (RSD<1%) (SM). The trial 3 method(normal trial 1 method with transducer regulated compaction) was firsttested on the Cambisol-Lucern soil for SOM ranging at low concentrationsfrom 0 to approximately 5% and it produced a better and tighterregression than what was produced in trials 1 and 2. Trial 3 was thenconducted on the natural Ferrosol soil samples producing quantitativedata using simple linear regression. Of the different combinations ofdata used for soil organic content determination, utilizing the PXRFdata in conjunction with the Z-Plane data resulted in the highestquality results.

With the ushering of the maker revolution (Anderson, 2013; Hatch, 2014)the cost of prototyping scientific instrumentation has decreasedconsiderably enabling scientists to inventively and economically attacksome of the world's biggest problems (Kwon and Lee, 2017; Sedlak, 2018)such as climate change. Despite room for potential enhancements to thisprimary prototype and method (SM), on average and across all trials, theZ-plane instrument produced volumetric SOM data via simple linearregression that was comparable with costlier superficial Vis-NIRmultivariate instrumentation. The Z-Plane sensor effectively enabledparallel acquisition of volumetric SOM data and elemental compositionsvia PXRF. On average, the Z-Plane instrument attachment prototypeoutperformed our Vis-NIR device (TerraSpec-PLSR) and was constructed ata cost of approximately 100 U.S. dollars (USD) which is much lower thanwhat one might expect to pay for scientific equipment capable ofquantifying SOM. Foregoing the PXRF device and its associated data, weestimate that a standalone version of this instrument possessing its ownX-ray generator can be produced at an additional cost of approximately300 USD (Science Buddies Staff, 2017). The maker revolution and theassociated availability of online resources, integrated circuits,sensors, electronic components, 3D printing and circuit boarddevelopment capabilities offer scientists an avenue to developspecialized instrumentation pushing the frontiers of science at a lowermonetary burden (American Association for the Advancement of Science(AAAS) and Jarvis, 2011; Anderson, 2013; Hatch, 2014; Kwon and Lee,2017; Science Buddies Staff, 2017; Sedlak, 2018). We use our low-costinstrument attachment as evidence of this and direct attention to itsexcellent regression reproducibility and soil specific linearity ofresponse to variations in SOM The device could potentially empowerscientists with the ability to perform low cost, rapid, high throughputanalyses and may be especially useful in cases where dense sitecharacterization (Taylor et al., 2004) of soil organic content isdesired such as for SOC sequestration and the UNFCCC's climate changemitigation monitoring and benchmarking purposes.

Sample Preparation: Vertisol, Cambisol, sand, Ferrosol

The procedures for soil sample pre-preparations are described below.

The soil sample (Vertisol, Cambisol, sand) is grinded in a large ballmill for 1 hour to de-clump and homogenize the soil.

The milled soil sample is sieved using a mechanical sieve. The lastsieve in the stack is a 250-micron mesh. This is done to isolate the<250-micron fraction of soil and to further homogenize the soil sample.

The sieved soil sample is dried in a soil drying oven at 105-110 degreesCelsius for 24 hours.

The sample is removed from the oven and further homogenized using ariffle splitter 5 times (Schumacher et al., 1990).

The sample is ignited at 440 degrees Celcius as recommended by ASTM(2014) for 24 hours.

After ignition, the sample is removed from the oven and allowed tosufficiently cool before being homogenized once again 5 times using ariffle splitter (Schumacher et al., 1990). The sample should now behomogenized and clear of any organic matter.

The sample is then transferred to a plastic container for use in theincremental surrogate addition steps.

Note: Trial 3 used a natural Ferrosol soil and was not subjected tothese preparation procedures. As is common for soil analyses, theFerrosol was sampled from the field, dried at 40 degrees Celsius for48h, sorted to the <2 mm fraction, and crushed to <100-microns inpreparation for subsequent PXRF, Z-Plane and LECO analyses (Wilson etal., 2017).

Sample Preparation: Incremental Surrogate Addition (Vertisol, Cambisol,sand)

Trials 1 and 2 were conducted at different points in time but theseinstructions apply to both cases with the exception that for trial 2,sample vials were filled completely.

The procedures for incremental organic matter addition to soils is asfollows:

Draw a line associated with a 15 mm sample depth on the XRF analysisvials.

Fill a vial to the indicated line with an ignited and homogenized soilsample (Vertisol, Cambisol, sand). Cap and label the vial with theassociated soil type, surrogate, and organic matter content of the soil.

Pour the remaining ignited soil into a previously weighed vessel with acap which will serve as a mixing vessel for subsequent steps. The newvessel weight minus its empty weight is the weight of soil containedwithin the vessel.

Sieve the surrogate you will be using (commercial powdered white sucroseor powdered Lucerne) with a 250-micron sieve. Retain the <250-micronfraction for subsequent steps.

Using the equation employed by Ravansari and Lemke (2018) prepare thenext incrementally spiked sample by placing the soil vessel on thescale. Prepare the next sample by adding a sufficient amount ofsurrogate to the mixing vessel to increase soil organic matter contentby the desired percentage. The difference in vessel weight before andafter surrogate addition is the weight of the surrogate added. Therelevant equation employed by Ravansari and Lemke (2018) is as follows.OM′=((OM*W)+S)/(W+S)

Where OM′ is the sample organic matter fraction after surrogateaddition, OM is the organic matter fraction of the sample prior toorganic matter addition, W is the weight of the sample prior to organicmatter addition and S is the weight of the surrogate added to thesample.

Cap the vessel and shake sample to mix the added surrogate. This acts asa premix step.

Pour the vessel contents onto a square piece of construction paper forhomogenization. Carefully give the cap and vessel light taps on theconstruction paper to ensure complete transfer of the sample to thepaper. Roll the sample over on itself 20 times to homogenize the sample(Piorek, 1998).

Transfer a homogenized aliquot of the sample into a new PXRF analysisvial filling it to the 15-mm line. Label the vial with the associatedsoil type, surrogate, and organic matter content of the soil. Transferthe rest of homogenized sample back to the mixing vessel.

Repeat steps 5 through 9 until the sample has been spiked to the desiredorganic matter content (20%).

Construction of Compactor Apparatus for Trial 2 Analyses

A compactor apparatus was constructed to control and characterize sampledepths and densities. It was constructed using the plunger end of asyringe with its black rubber removed. An XRF analysis vial was cut fromthe bottom and placed on a smooth plastic surface. It was filled withepoxy resin and the syringe plunger was inserted into the vial. Theresin was left to harden and then the XRF analysis vial was cut awayusing a razor, this created a mold of the analysis vial interiorconsisting of a smooth base. Trial 2 samples used this plunger tocompact samples.

Transducer Apparatus for Trial 2 and 3 Analyses

An apparatus was constructed and employed for trial 2 samples to delivera set amount of energy to the samples prior to compaction using thepreviously described compactor apparatus. The delivery of this energy tothe samples serves as a compaction step itself prior to pushing theplunger on the compactor because it removes potential air pockets andincreases uniformity across samples. A surface transducer (GD003)manufactured by Shenzhen Huihongsheng Electronics Co (China) was wiredup to a generic LM386 module and connected to laptop audio output.Python code was used to generate a signal which was sent the transducer,the signal consisted of a 5 second 300 hz burst and then a linear chirpsignal was applied which varied between 1-500 hz over 40 seconds. Thiscode can be found in the supplemental text. Trial 3 also employed thisapparatus using a shorter cylindrical vessel.

Analysis of Samples (Trial 1 and Trial 3)

Trial 1 and trial 3 procedures are identical except for step 4. Noduplicated measurements were conducted for trial 3. Trial specificinstructions are provided below (namely step 4 and step 7).

1. Uncap and place an X-Ray thin film mylar cover 1.5 μm [SOMAR-FILMMicro-Plus Mylar, Sietronics Pty Ltd. (Australia)] over the analysisvial opening and place a rubber band around the analysis vial with fourloops positioning the rubber band uniformly at the top edge of samplevial.2. Cut the excess X-Ray thin film mylar around the rubber band.3. Give the analysis vial a few shakes using a rolling motion and an upand down motion alternating between the two. The objective is tore-homogenize the sample.4. Trial 1: Turn over the analysis vial (mylar side down) and give thevial 3 light taps on a wooden surface to remove any air pockets andensure that the sample is evenly spread out at the top and bottom of theanalysis vial.Trial 3: Turn over the analysis vial (mylar side down) and place it inthe previously described transducer apparatus. Run the associated codefor the transducer and allow it to finish.5. Place the analysis vial onto the PXRF analyzer window while givingthe vial a push and a twist into the platform grove to ensure that thesample placement is consistent throughout all experiments.6. Analyze the sample with the PXRF instrument and the attachedradiation detector. Note: Analysts should start logging data from theZ-Plane sensor before the PXRF instrument begins analyzing the sample.7. Trial 1: Perform steps 3 through 6 again to obtain a duplicatemeasurement of the sample between a re-homogenization event.Trial 2: No duplicate measurement taken for trial 3.8. Cap, save and store analyzed samples for potential futureexperiments.Analysis of Samples (Trial 2)

This section requires the constructed plunger described in previoussections. Refer to “Construction of Compactor Apparatus for Trial 2Analyses”. Sample vial radius is required for subsequent computations (1cm). This procedure requires sample vials to be cut from the bottom. Inthe interest of saving resources, the same vial was re-used betweenanalyses after thoroughly cleaning them with tap water and drying withpaper towels. Vials were discarded and new vials utilized for differentsoil/surrogate combinations. These samples are not sensitive to crosscontamination because the analyte is organic matter which is present atthe percent levels. In addition, the compactor and vials are made fromplastics and polymers and do not absorb water.

1. Cut out the bottom of an analysis vial using a razor (henceforthreferred to as the “cut end”).

2. Place two sheets of Mylar X-Ray thin film on the plunger's resin andinsert the plunger into the cut end of the vial. The Mylar on theplunger serves to inhibit soil from sliding into the area between theanalysis vial and resin. Extra Mylar should be cut away.

3. Using a sharp razor create a small slit in the analysis vial at thetop edge (opposite cut end) approximately 7 mm away from edge to serveas an air release valve when samples are compacted within the vials.

4. Take note of the prepared compactor mass.

5. Transfer the relevant soil sample into the compactor in preparationfor analyses filling it up as much as possible.

6. Take note of the prepared compactor mass again to enable calculationof the soil mass contained within the compactor.

7. Place an X-Ray thin film mylar cover 1.5 μm [SOMAR-FILM Micro-PlusMylar, Sietronics Pty Ltd. (Australia)] over the analysis vial openingand place a rubber band around the analysis vial with four loopspositioning the rubber band uniformly at the top edge of the samplevial.

8. Overturn the sample and place the sample into the transducerapparatus. Run the python code and when finished remove the sample andplace the bottom of the sample on a hard table surface. Proceed to applypressure to compact the sample as much as possible.

9. Analyze the sample using the PXRF and the Z-Plane detector.

10. Remove sample from analysis platform and remove the protective Mylarand rubber band. Return the sample contents to its designated vial forpotential future use. Tap lightly to remove compactor contents but donot allow compactor to move.

11. After the compactor has been emptied, determine the mass of thecompactor. It will be slightly more than before as there will benegligible remnants within the vial.

12. Fill the compactor with water minimizing meniscus effects visuallyand determine the mass of the filled compactor. Subtract the filled massand the empty masses from one another to determine the mass of water.Use this mass of water along with the density of water at 20 degreesCelcius to compute the volume of the compactor.

13. Sample bulk density during analyses can then be computed by divingcompactor soil mass by compactor water volume.

14. Sample depths can also be computed from compactor volume byrecognizing that the volume of the cylindrical vials are V=h*pi*r2.Rearranged for depth this becomes h=V/(pi*r2) where V is the computedcompactor volume, r is the vial radius and h is the sample depth.

General Considerations and Notes for Z-Plane Sensor:

A standard should be run periodically throughout experiments and it isrecommended that a standard be analyzed between approximately every 10sample measurements to allow the analyst to check for instrumental drift(Brand and Brand, 2014). The chosen standard is the 0% organic mattersand sample from the trial 1 sand-sucrose experiment and was analyzedthroughout all experiments.

The PXRF is operated at 40 kv and 10 microamp settings. The PXRF is aBruker Tracer III-SD.

The PXRF and Z-Plane analyses are conducted for a duration of 3 minuteseach.

A freeware serial monitoring program called “Cool Term” is used to logdata however many serial monitors exists and can be used instead. Theradiation detector is wired to an Arduino Mega 2560 Rev 3 which isconnected to a laptop via USB connection. The serial monitoring programis used to log data from the communications port. Data from the sensoris sent to the Arduino and the Arduino sends the information to thelaptop. The relevant code for Z-Plane detector operation is provided inthe supplementary text.

Vis-NIR Processing

A Terraspec Vis-NIR device was used to perform 3 replicate scans (10seconds per scan) on trial 2 and 3 samples. The handheld probe was notmoved between replicate scans. The samples were poured on a sheet ofpaper and gently flattened with a piece of wood that was wrapped withsaran wrap. Saran wrap was also used as a protective barrier between thecontact probe and the samples. The splice corrected replicate Vis-NIRdata was averaged into a single spectrum using python code. Themanufacturer specified Terraspec spectral resolution (Full width halfmaximum) is 3 nm at 700 nm, 6 nm at 1400 nm and 6 nm at 2100 nm. Thebins associated with noise were not used in the modelling, i.e.Terraspec bins [350-2500 nm] were cut down to [402-2220 nm] (Ellinger etal., 2019). RS3 software version 6.0.7 was used to acquire the spectrafor the samples. ViewSpec Pro software version 6.0.9 was used to performsplice processing. TSG Professional software version 7.0.1.062 was usedto export spectral data to csv format for subsequent modelling. Partialleast square regression (PLSR) and leave one out validation wasperformed in Matlab version R2017b (performance statistics summarized inTable 1).

Partial Least Squares Regression

Models were created from the various (Vis-NIR, PXRF, Z-Plane) spectraldata using partial least squares regression with leave one out. Modelswere constructed using the processed Vis-NIR data, raw PXRF spectraldata consisting of 2048 bins associated with energy range of 0-40 kV,and Z-Plane sensor data (single dimensional). Various blends of thesedata were modelled using PLSR and the methods used to integrate thedisparate data for the various combinations are discussed.

The previously described processed Vis-NIR spectral data was modeledusing PLSR without further treatment.

The previously described PXRF spectral data was modeled using PLSRwithout further treatment.

The Z-Plane counts alone were not modeled using PLSR but were modeledusing simple linear regression instead.

Typically, analytes are directly predicted using multidimensional datain conjunction with PLSR. Integration of disparate multidimensional data(PXRF and Vis-NIR spectra) and single dimensional Z-Plane data wasachieved using a novel multivariate version of the Ravansari-Lemkecalibration method, referred to as RL-PLSR. The multidimensionalspectral data was used to predict correction coefficients formultiplication to Z-Plane SLR determined organic measurements to correctthem and get them to where they ought to be. It is a method formitigating variability in the SLR using the information contained in thespectra. To maintain independence of the calibrations and validationsand avoid a circular logical fallacy, these correction coefficients werecomputed from the SLR (using stepwise leave one out) and predicted viaPLSR (also using leave one out). The predicted coefficients were thenapplied to the SLR determined analyte measurements (Fig. S 5). RL-PLSRis essentially a method for fine tuning anchored baseline SLR analytemeasurements using multidimensional data (where the multidimensionaldata is used to predict correction coefficients via multivariate methodsfor subsequent application to baseline values obtained via simplermethods). The multivariate version of the Ravansari-Lemke calibrationmethod may be usefully applied to different circumstances where baselinevalues are available (e.g. RL-multivariate to predict coefficients forapplication to baseline PXRF rendered total concentrations forbioavailability prediction). This method was used for the“Z-Plane+PXRF”, “Z-Plane+Vis-NIR”, and “Z-Plane+PXRF+VisNIR”combinations. The integration of disparate PXRF and Vis-NIR spectra arediscussed below.

Integration of the disparate PXRF and VIS-NIR spectra was achieved bysumming all bins in each spectra and dividing each individual bin by thesummed total for each spectra. The spectra thereby retain their shapesand the information contained within but are now on an even playingfield with each other. The two disparate spectra were then concatenatedfor subsequent use in PLSR (PXRF+Vis-NIR) or RL-PLSR(Z-Plane+PXRF+Vis-NIR) procedures.

The number of components used for the various multivariate PLSR modelswere determined by balancing model parsimony and minimizing the meansquare prediction error of the model.

Refrigeration of Prepared Samples

Trial 1 samples were analyzed after their creation but were not storedin a refrigerator. Over time this may lead to changes in SOM content forthose samples due to mineralization processes. While not in use, trial 2samples were stored in a refrigerator at all times after their creationto prevent potential mineralization. It is recommended that analystsrefrigerate prepared samples to preserve them for potential futureexperimentation.

The mounting platform immobilizes the sensor within the Z-plane forconsistency throughout experiments. The groove in the mounting platformfits the analysis vials and the objective of the groove is to ensureconsistent sample placement throughout the experiments becausevariations in sample distance from the sensor can affect results.

A closeup picture of the mounting platform and sensor can be viewed inFig. S 3 and Fig. S 4. The objective of the mounting platform is simplyto immobilize the sensor within the Z-plane for consistency throughoutexperiments. The groove in the mounting platform fits the analysis vialsand the objective of the groove is to ensure consistent sample placementthroughout the experiments because variations in sample distance fromthe sensor can affect results.

Transducer Signal Code (Python)

This code was used for the previously discussed surface transducer.

import pyaudio import numpy as np from scipy.signal import chirp p =pyaudio.PyAudio( ) fs = 350000 x = np.linspace(0, 5, 1750000) y =chirp(x, f0=300, f1=300, t1=5, method=‘linear’) z =y.astype(np.float32).tobytes( ) t = np.linspace(0, 40, 14000000) w =chirp(t, f0=1, f1=500, t1=40, method=‘linear’) q =w.astype(np.float32).tobytes( ) stream =p.open(format=pyaudio.paFloat32, channels=1, rate=fs, output=True)stream.write(z) stream.write(q) stream.stop_stream( ) stream.close( )p.terminate( )Statistics

Minitab version 18.1 was used for regression significance testing ofreplicate regressions constructed for trial 1, for regressionsignificance testing of different soil-surrogate responses, and forcontrol chart generation to check for sensor drift. Matlab versionR2017b was used for PLSR modeling of the Vis-NIR data, RSDdeterminations for all regressions, and some plot preparation andpresentation. Excel 2016 was used for some table and plotpreparation/presentation in addition to basic arithmetic procedures.

Table S 3 summarizes the equations employed in the computations of themetrics presented in Table 1 and Table 2 of the main text.

A 0% SOM sand sample was adopted as a standard and was analyzedregularly throughout the experiments to check for sensor drift (Brandand Brand, 2014; Kenna et al., 2011). A control chart was constructed totrack the sensor's response to the adopted standard, it was constructedusing Minitab statistical software and is displayed in Fig. S 6. Twooutliers were identified which were beyond control limits for the sampleaverage value. Outliers are not deemed to be a result of sensor driftbut rather due to human operator inconsistent taps as elucidated in thediscussion section.

To assess analytical repeatability under identical conditions theadopted standard was run in triplicate without re-homogenization betweenanalyses resulting in a coefficient of variation (CV) of <1%.

A Horowitz curve was constructed comparing trial 1 20% Cambisol-sucrosesample residual as a function of its sample depth. Sample depths werecontrolled using the method from trial 2. Residuals were computed usingthe Cambisol regression from trial 1. Empirically, based on the Horowitzcurve a sample depth beyond 1.2 cm should not affect Z-Plane sensormeasurements. The 1.2 cm sample depth threshold was computed byperforming a first order derivative test on the fitted second orderpolynomial regression to identify its critical point. The 1.2 cmthreshold was adopted as the critical threshold depth for allsoil-surrogate combinations because it was later determined that thesensor's height is approximately 1.2 cm from the PXRF analysis platformreinforcing this empirical finding (i.e. if the entirety of the sensorheight is covered by a sample's depth then sample depth should not makea difference in the rendered Z-Plane counts).

For all regression significance tests, P values<0.01 are consideredsignificant. The P value has been adjusted from the typical 0.05 to 0.01to account for the multiple comparisons drawn within the experiments.

Trial 1 regression significance testing was performed on regressionsconstructed using replicate measurements on the same samples betweenre-homogenization events to assess repeatability (n=31 vs 31). Thesetests revealed no significant differences between comparisons (allP>0.23).

Trial 1 regression significance testing comparing differentsoils-surrogate combinations was performed on the 31 available datapoints which were generated by averaging the two available replicatemeasurements for every sample (n=31 vs 31). Results indicate asignificant soil type dependent response (all P<0.001) but surrogatetype effects are not significant (all P>0.01).

Trial 2 regression significance testing comparing regressions fordifferent soil-surrogate combinations was performed on the 21 availabledata points which include analytical replicates both with and withoutre-homogenization for the highest and lowest non-zero SOM samples (n=21vs 21). Results indicate a significant soil and surrogate type dependentresponse (all P<0.001).

For trial 2 bulk density (BD) was characterized which enableddevelopment of plots relating Z-Plane sensor response to soil BD. Therewas a strong relationship between soil BD and counts.

Cook's distance is often used to identify potential outliers in simplelinear regression (Cook, 1979). For the trial 3 (natural Ferrosol)simple linear regression of Z-Plane counts against SOC, three potentialoutliers were identified where the computed cook's distances weregreater than 4/(n−k−1) (where n is the number of samples used toconstruct the regression and k is the number of independent variables).Of these identified outliers, the single most influential point on RSDwas removed from the dataset. A potential cause of outliers within thedatasets may be due to the human introduced variability caused by BDalteration when the samples were removed from the transducer forplacement into the analysis platform. This highlights another advantageof integrating the transducer into the analysis platform as discussed inthe “Transducer Integration” section of these supplementary materials.

Measurement Time

When the sensor is operational, it is continuously streaming data intobins. The samples were analyzed for a total of three minutes however,lower analysis times may yield similar results as depicted in Fig. S 10.Using samples from the sand-sucrose experiment, we show that similarregression coefficients of determination were produced using discretebin numbers associated with different measurement integration times.This suggests that measurement time can be greatly decreased and opensthe possibility of using short X-ray pulses to obtain of soil organiccontent information.

Standalone Device Embodiment

Amptek's “Cool-X” pyroelectric X-ray generator may be useful in thedevelopment of a standalone instrument because the element isapproximately the size of a penny (Amptek, 2019) and would be wellsuited for portable instrumentation (although it may currently be costprohibitive). Another option is to utilize a relatively inexpensivecathode ray tube and high voltage generator to replace the PXRFcomponent for X-ray generation. Both these options suffer from X-rayflux variability over time and temperature which fluctuate depending onoperational circumstances. This variability can perhaps be mitigated byimplementing Peltier cooling elements although this solution willrequire a great deal of power which may not be ideal for current batterytechnology. Given the relatively high heat capacity of liquid water,another option is to implement water cooling elements whereby a smallisolated storage tank of water (−0.5 L) is placed in contact withcorrosion resistant thermally conductive cooling elements that are incontact with the heat generating elements. The heat generated fromoperation would then be transferred to the water and the water could bereplaced as needed when it reaches some predefined temperature(instrument could be programmed to monitor water temperature and haltanalyses/alert analyst as needed). Another option is to monitortemperatures and X-ray flux using another X-ray sensor placed adjacentto the X-ray source so that the device consists of two separate X-raysensors. One of the sensors would monitor the flux of X-rays from thesource and the other would be used to analyze the sample. Variability inmeasurements due to variability in the flux of the X-ray source couldthen be mitigated using the SLR version of the Ravansari-Lemkecalibration method (Ravansari and Lemke, 2018). Such a standalone devicemay also have an integrated transducer at the sample platform toregulate sample compaction.

Transducer Integration

Regulating compaction of the samples was determined to improve results(FIG. 2). Therefore, for the natural soil samples (trial 3) this wasachieved by placing normally prepared samples into the transducerapparatus prior to analyses. Vibrating samples can however affect thegrain size distribution within the vial because different grain sizefractions may sort/vertically stratify within the vial as a function oftransducer parameters such as amplitude, frequency and time ofoperation. Different grain size fractions also have been shown topossess different elemental concentrations. This sorting phenomenonintroduces an interesting possibility for potential rapid determinationof soil grain size distribution as soil texture was shown to exert asignificant effect on detected counts (i.e. sort grain size distributionusing transducer and analyze detected counts vertically along vial byemploying a mobile Z-Plane sensor with a collimator). If an energydispersive mobilized Z-Plane detector with collimation is employed,determination of elemental concentrations within different grain sizefractions also becomes a possibility. Currently, soil grain sizeanalyses can take up to 16 hours to conduct so adapting PXRF to generaterapid grain size distribution information would be beneficial. Thesepotential issues and opportunities pertain to un-grinded sampleshowever, in preparation for ex-situ analyses, samples are often grindedand so these opportunities are not possible and the stated issues are ofconcern. These issues can be circumvented entirely by integrating thetransducer with the sample holder assembly and seamlessly activating thetransducer after having performed PXRF analysis. Implementation of thissuggestion would preclude the analysis from being a truly “parallel”analysis however, with manufacturer support this can be a seamlessintegrated system that produces a great deal of useful soil informationthan what is currently possible with rapid PXRF instrumentation.Nonetheless, the results of these experiments demonstrate that goodcalibrations can be developed for soil organic content quantificationusing the employed methods. Identification of optimal transduceroperation time, amplitude and frequency characteristics and transducereffects on PXRF concentration measurements when collimation isimplemented between sample and mobilized Z-Plane detector should beexplored in future work.

Discussion

The analytical repeatability of the sensor's response was excellent(CV<1%). Similar to PXRF geochemical measurements, much highervariability is observed when samples are disturbed between analyses(Ravansari and Lemke, 2018). This may be due to heterogeneity and/orother potential sources of analytical variability such as sample BDvariations when analysis vials are overturned and placed in the analysisplatform between re-homogenization events. For trial 1, as is common tosoil PXRF analyses, sample vials were overturned and tapped on a cleansurface to ensure a smooth PXRF area of analysis and removal ofpotential air pockets. The method employed for trial 1 is much moreconvenient, less time consuming, and produced better results as comparedto the BD controlled method employed in trial 2. The method employed fortrial 1 however, suffers from the potential introduction of additionalvariability due to inconsistent human taps. To remove the possibility ofmeasurement variation due to inconsistent human taps, trial 3 exploredemploying the trial 1 method with transducer regulated compaction inlieu of human taps. This greatly improved the quality of theregressions. This may be especially important for this device and methodbecause there is much higher three-dimensionality to the analyses andvariable human taps may cause inconsistent compaction and BD variationswhich in turn affect the counts detected at the Z-plane sensor. This isfurther highlighted by the control chart where 2 data points wereidentified as beyond control limits indicating a high degree of randomvariability events which we attribute to inconsistent operator taps. Theoutliers indicated by the control chart are interpreted as humaninconsistency events as opposed to sensor drift because the experimentalmeasurements conducted between those standard runs still resulted invery high coefficients of determination. The <1% CV achieved betweenreplicate runs without re-homogenization events also hints that theobserved variability is due to the re-homogenization or sample placementstep. This source of random variability likely exists in the computedregressions as well affecting precision of the method but the trendsclearly indicate a good average linear response to increasing SOMcontent. We thus postulate that precision can be increased andcalibrations improved by further controlling such sources of randomvariability associated with sample vial preparation and placement. Thisproof of concept device demonstrates the potential feasibility of usingan X-Ray sensor in the Z-plane to extract sample information but if lowlimits of detection and high accuracy are to be achieved, measurementprecision must be increased by eliminating random variability.

Nevertheless, the sensor is responding accordingly to variations in soilorganic content regardless of surrogate type but there is a strong soiltype dependent response observed highlighting the potential viability ofsite specific calibrations or preset calibrations for popular soiltypes. Variation in SOM has been shown to cause elementally specificdeviation in PXRF geochemical measurement accuracy (Ravansari and Lemke,2018). These deviations may be accounted for by employing SOM correctionprocedures described by Ravansari and Lemke (2018). The correctionprocedures require the quantification of SOM for all samples which iscostly and time consuming. This instrument attachment may be used torapidly and concurrently obtain SOM information during PXRF analyseswhich can then be used in conjunction with onboard device computers toseamlessly compute and apply appropriate corrections via onboardalgorithms. The described process can potentially result in moreaccurate PXRF geochemical measurements in addition to SOM and SOCinformation thus advancing the utility of PXRF instrumentation. Whereelemental concentrations are not required, the PXRF component of theinstrument can be replaced with a simple X-Ray source allowing itsoperation as a standalone instrument presumably further increasingportability and decreasing costs.

The instrument discussed in this manuscript was developed as a proof ofconcept for use in the laboratory however, we also suggest thedevelopment of a soil specific intrusive instrument attachment for PXRFdevices specifically for in-situ applications. It is assumed thatin-situ variability in soil moisture may further complicate deviceperformance by affecting SOM determinations. We postulate that theseeffects may be mitigated by simultaneously quantifying soil water anddeveloping calibrations. Integration of an intrusive soil water probe isthus a potentially welcomed amalgamation because it may facilitatein-situ applications. Soil water has also been shown to decrease in-situPXRF geochemical measurement accuracy. The integration of a water probewould enable parallel soil water content determination, and correctionsto PXRF geochemical measurements could be applied using an analogousextension of Ravansari and Lemke's (Ravansari and Lemke, 2018) SOMcorrections adapted to soil water content. Development of a hybrid soilspecific PXRF device consisting of an intrusive radiation detector andsoil water probe is suggested because this would potentially enablequantification of SOM, soil water content, and more accurate soilelemental concentrations (because elemental concentrations could becorrected for SOM and water contents via streamlined onboardalgorithms).

Trial 1 produced quantitative results. Sample vials in trial 1 werefilled to a consistent sample depth however, as previously discussed,tapping the vials for PXRF analyses may affect sample compaction and BDwhich can introduce random variability and ultimately affectmeasurements. Development of an objective repeatable method andapparatus for controlling compaction uniformly (trial 3) enabled betterquantification of soil organic content.

The development of this device presents an interesting opportunity foraugmenting of PXRF measurement capabilities because in addition to thepotential for SOM corrections which have been discussed, othercorrections may be applied as well. Compton normalization andfundamental parameters are calibration procedures which are employed forsome PXRF devices and are used to render measurements or otherwisemitigate measurement deviation by accounting for variability in samplematrix composition (USEPA, 2007). The information rendered by thisdevice may be potentially used in a similar fashion to increase accuracyby mitigating variability in sample matrix composition. Conversely, thePXRF spectra that is simultaneously generated during analyses may beused to increase the attachment's accuracy as well because it can informalgorithms of sample composition for potential accountability. Theinterplay and advantages of using one device's information to augmentthe other was explored in this study and should be further exploredusing both simple and multivariate techniques including fundamentalparameters-esque or Compton normalization-esque adaptations. Finally,the sensor employed in this investigation detected incident radiation inthe Z-plane irrespective of its energy level (for its sensitive range)however, despite its low cost, it is potentially capable of beingemployed as a crude energy dispersive detector as well. An interestingextension of this work would be to employ the detector as an energydispersive sensor to extract further information from samples undergoingX-Ray analyses such as vertical stratification of analytes. Other moreadvanced energy dispersive sensors such as Amptek's X-123 sensor (Reduset al., 2006; Redus et al., 2009) adapted for employment in the Z-planecould potentially enable enhanced data acquisition and better SOMquantification from samples undergoing PXRF analyses. Employment of aZ-Plane detector may require a redesign of sample vials used for PXRFanalyses. A new type of vial is suggested which is comprised of asidewall or side-stripe which is made of a high a strength polymer orgraphene. Such a vial may aide Z-Plane measurements as it will reducethe attenuation and scattering of desirable signals for better detectionat the Z-Plane detector. The results of this preliminary work warrantfurther investigation into the feasibility of such a Z-plane radiationsensor for soil chemical characterization via either generic, soilspecific, or site specific parsimonious simple linear regressions, oreven standalone and/or mix and matched hybrid multivariate techniquesutilizing PXRF spectra and Z-Plane sensor data.

It will be apparent to one with skill in the art that the mattercharacterization systems and methods may be provided using some or allof the mentioned features and components without departing from thespirit and scope of the present invention. It will also be apparent tothe skilled artisan that the embodiments described above are specificexamples of a single broader invention which may have greater scope thanany of the singular descriptions taught. There may be many alterationsmade in the descriptions without departing from the spirit and scope ofthe present invention.

REFERENCES

-   Adams, W. A., 1973. THE EFFECT OF ORGANIC MATTER ON THE BULK AND    TRUE DENSITIES OF SOME UNCULTIVATED PODZOLIC SOILS. Journal of Soil    Science 24(1), 10-17.-   American Association for the Advancement of Science (AAAS), Jarvis,    M., 2011. Science Selects ‘Science Buddies’ Web site to Win SPORE    Award|Available at:    https://www.aaas.org/news/science-selects-science-buddies-web-site-win-spore-award    [Accessed: 29 Apr. 2019]. (April 29).-   Anderson, C., 2013. Maker Movement, Wired. Conde Nast Publications,    Inc., San Francisco, pp. 106-n/a.-   Assunção, S. A., Pereira, M. G., Rosset, J. S., Berbara, R. L. L.,    Garcia, A. C., 2019. Carbon input and the structural quality of soil    organic matter as a function of agricultural management in a    tropical climate region of Brazil. Science of The Total Environment    658, 901-911.-   Australia New Zealand Banking Group—ANZ, 2018. Carbon    Trading—ANZ|Available at:    http://www.anz.com/corporate/markets/carbon-trading/[Accessed: 21    Aug. 2018]. (21-08-2018).-   Australian Federal Government—Australia, 2018. Carbon Credits    (Carbon Farming Initiative—Measurement of Soil Carbon Sequestration    in Agricultural Systems) Methodology Determination    2018—F2018L00089|Available at:    https://www.legislation.gov.au/Series/F2018L00089 [Accessed: 21 Aug.    2018].-   Bentler, P. M., Mooijaart, A., 1989. Choice of structural model via    parsimony: A rationale based on precision. Psychological Bulletin    106(2), 315-317.-   DAFWA, 2013. Report card on sustainable natural resource use in    Agriculture, Australian Department of Agriculture and Food Western    Australia.-   Gałuszka, A., Migaszewski, Z. M., Namieśnik, J., 2015. Moving your    laboratories to the field—Advantages and limitations of the use of    field portable instruments in environmental sample analysis.    Environmental Research 140, 593-603.-   Hatch, M., 2014. The maker movement manifesto: Rules for innovation    in the new world of crafters, hackers, and tinkerers. McGraw-Hill    Education New York.-   IUSS Working Group, W., 2006. World reference base for soil    resources. World Soil Resources Report 103.-   Kwon, B.-R., Lee, J., 2017. What makes a maker: the motivation for    the maker movement in ICT. Information Technology for Development    23(2), 318-335.-   Lal, R., 2006. Enhancing crop yields in the developing countries    through restoration of the soil organic carbon pool in agricultural    lands. Land Degradation & Development 17(2), 197-209.-   Parsons, C., Margui Grabulosa, E., Pili, E., Floor, G. H.,    Roman-Ross, G., Charlet, L., 2013. Quantification of trace arsenic    in soils by field-portable X-ray fluorescence spectrometry:    Considerations for sample preparation and measurement conditions.    Journal of Hazardous Materials 262(Supplement C), 1213-1222.-   Potts, P. J., West, M., 2008. Portable X-ray Fluorescence    Spectrometry: Capabilities for in Situ Analysis. RSC Pub.-   Ravansari, R., Lemke, L. D., 2018. Portable X-ray fluorescence trace    metal measurement in organic rich soils: pXRF response as a function    of organic matter fraction. Geoderma 319, 175-184.-   Reeves, D. W., 1997. The role of soil organic matter in maintaining    soil quality in continuous cropping systems. Soil and Tillage    Research 43(1), 131-167.-   Rouillon, M., Taylor, M. P., 2016. Can field portable X-ray    fluorescence (pXRF) produce high quality data for application in    environmental contamination research? Environmental Pollution    214(Supplement C), 255-264.-   Rouillon, M., Taylor, M. P., Dong, C., 2017. Reducing risk and    increasing confidence of decision making at a lower cost: In-situ    pXRF assessment of metal-contaminated sites. Environmental Pollution    229, 780-789.-   Rumpel, C., Amiraslani, F., Koutika, L.-S., Smith, P., Whitehead,    D., Wollenberg, E., 2018. Put more carbon in soils to meet Paris    climate pledges. Nature 564.-   Saini, G. R., 1966. Organic Matter as a Measure of Bulk Density of    Soil. Nature 210(5042), 1295-1296.-   Science Buddies Staff, 2017. How to Build an X-ray Machine|Available    at:    https://www.sciencebuddies.org/science-fair-projects/project-ideas/Phys_p083/physics/how-to-build-an-x-ray-machine    # summary [Accessed: 28 Apr. 2019]. (April 28).-   Sedlak, D. L., 2018. Disruptive Environmental Research.    Environmental Science & Technology 52(15), 8059-8060.-   Soriano-Disla, J. M., Janik, L. J., Viscarra Rossel, R. A.,    Macdonald, L. M., McLaughlin, M. J., 2014. The Performance of    Visible, Near-, and Mid-Infrared Reflectance Spectroscopy for    Prediction of Soil Physical, Chemical, and Biological Properties.    Applied Spectroscopy Reviews 49(2), 139-186.-   Taylor, P. D., Ramsey, M. H., Potts, P. J., 2004. Balancing    Measurement Uncertainty against Financial Benefits: Comparison of In    Situ and Ex Situ Analysis of Contaminated Land. Environmental    Science & Technology 38(24), 6824-6831.-   UNFCCC, 2016. Paris Agreement—Status of Ratification|UNFCCC—United    Nations Framework for Convention on Climate Change|Available at:    https://unfccc.int/process/the-paris-agreement/status-of-ratification    [Accessed 21 Aug. 2018]. (21-08-2018).-   USEPA, 1998. Environmental technology verification report. Field    portable X-ray fluorescence analyzer. Metorex XMET 920-P and 940,    EPA/600/R-97/146, United States Environmental Protection Agency.-   van Groenigen, J. W., van Kessel, C., Hungate, B. A., Oenema, O.,    Powlson, D. S., van Groenigen, K. J., 2017. Sequestering Soil    Organic Carbon: A Nitrogen Dilemma. Environmental Science &    Technology 51(9), 4738-4739.-   Viscarra Rossel, R. A., McGlynn, R. N., McBratney, A. B., 2006.    Determining the composition of mineral-organic mixes using    UV-vis-NIR diffuse reflectance spectroscopy. Geoderma 137(1), 70-82.-   Wang, D., Chakraborty, S., Weindorf, D.C., Li, B., Sharma, A., Paul,    S., Ali, M. N., 2015. Synthesized use of VisNIR DRS and PXRF for    soil characterization: Total carbon and total nitrogen. Geoderma    243-244(Supplement C), 157-167.-   B. A. Schumacher, K. C. Shines, J. V. Burton, M. L. Papp, Comparison    of Three Methods for Soil Homogenization. Soil Science Society of    America Journal 54, 1187-1190 (1990).-   ASTM, ASTM Method D2974-14: Standard Test Methods for Moisture, Ash,    and Organic Matter of Peat and Other Organic Soils. (2014).-   B. R. Wilson, D. King, I. Growns, M. Veeragathipillai, Climatically    driven change in soil carbon across a basalt landscape is restricted    to non-agricultural land use systems. Soil Research 55, 376-388    (2017).-   R. Ravansari, L. D. Lemke, Portable X-ray fluorescence trace metal    measurement in organic rich soils: pXRF response as a function of    organic matter fraction. Geoderma 319, 175-184 (2018).-   S. Piorek, in Current Protocols in Field Analytical Chemistry, V.    Lopez-Avila et al., Eds. (Wiley, 1998), chap. Section 3B.-   N. Brand, C. Brand, Performance comparison of portable XRF    instruments. Geochemistry: Exploration, Environment, Analysis,    2012-2172 (2014).-   M. Ellinger, I. Merbach, U. Werban, M. LieB, Error propagation in    spectrometric functions of soil organic carbon. SOIL Discuss. 2019,    1-25 (2019).-   T. C. Kenna et al., Evaluation and calibration of a Field Portable    X-Ray Fluorescence spectrometer for quantitative analysis of    siliciclastic soils and sediments. Journal of Analytical Atomic    Spectrometry 26, 395-405 (2011).-   R. D. Cook, Influential observations in linear regression. Journal    of the American Statistical Association 74, 169-174 (1979).-   Amptek, Amptek.com. (2019). COOL-X X-Ray Generator—Amptek—X-Ray    Detectors and Electronics. [online] Available at:    https://www.amptek.com/products/x-ray-sources/cool-x-pyroelectric-x-ray-generator    [Accessed 24 Aug. 2019]. (2019).-   USEPA, “EPA Method 6200 Field Portable X-Ray Fluorescence    Spectrometry for the Determination of Elemental Concentrations in    Soil and Sediment,” (United States Environmental Protection Agency,    2007).-   R. H. Redus, J. A. Pantazis, T. J. Pantazis, A. C. Huber, B. J.    Cross, Characterization of CdTe detectors for quantitative X-ray    spectroscopy. IEEE Transactions on Nuclear Science 56, 2524-2532    (2009).-   R. Redus, A. Huber, J. Pantazis, T. Pantazis, D. Sperry, in Nuclear    Science Symposium Conference Record, 2006. IEEE. (IEEE, 2006), vol.    6, pp. 3794-3797.-   J. Wetterlind, B. Stenberg, Near-infrared spectroscopy for    within-field soil characterization: small local calibrations    compared with national libraries spiked with local samples. European    Journal of Soil Science 61, 823-843 (2010).-   C. Kilbride, J. Poole, T. R. Hutchings, A comparison of Cu, Pb, As,    Cd, Zn, Fe, Ni and Mn determined by acid extraction/ICP-OES and ex    situ field portable X-ray fluorescence analyses. Environmental    Pollution 143, 16-23 (2006).

The invention claimed is:
 1. An apparatus comprising: a radiation sourceconfigured to irradiate a sample; an electric field sensor configured todetect an electric field of the sample during or after sampleirradiation by the radiation source, at least one property of theelectric field being altered by the sample irradiation; and an onboardcomputer communicably coupled to the electric field sensor and radiationsource, the onboard computer configured to determine one or more sampleproperties based on the electric field of the sample, the sampleproperties including at least one of physical or chemical composition ofthe sample.
 2. The apparatus in claim 1 wherein the radiation sourceproduces a monochromatic or polychromatic flux of radiation possessingenergies in a singular or plurality of regions of the electromagneticspectrum.
 3. The apparatus in claim 1 further comprising a wavelengthdispersive crystal used to control incident radiation energy from theradiation source onto the sample.
 4. An apparatus comprising: an X-rayfluorescence spectrometer configured to scan a sample; at least one ofan electric field sensor placed proximal to the sample configured toscan the sample by detecting an electric field of the sample during orafter sample irradiation by the X-ray fluorescence spectrometer, atleast one property of the electric field being altered by the sampleirradiation, and an X-ray radiation detector placed proximal to thesample configured to scan X-ray radiation traversing the sample; a frameconfigured to control a distance between the sample and at least one ofthe X-ray fluorescence spectrometer, electric field sensor, and X-rayradiation detector; and an onboard computer communicably coupled to theX-ray fluorescence spectrometer and at least one of the electric fieldsensor and the X-ray radiation detector, the onboard computer beingconfigured to determine one or more sample properties based on at leastone of the electric field and the X-Ray radiation traversing the sample,the sample properties including at least one of physical or chemicalcomposition of the sample.
 5. The apparatus in claim 4 wherein anelectromechanical transducer is communicably coupled with the one ormore processors and is activated by the one or more processors at leastone of before, during, between or after analyses.
 6. The apparatus inclaim 4 wherein the frame supports a core and the base of the frame ismobilized with a motor that is communicably coupled to the one or moreprocessors which activates the motor to move the core longitudinally atleast one of before, during, between or after analyses.
 7. The apparatusin claim 4 wherein a spectroscopy system is communicably coupled to theone or more processors and scan the sample, the information from thespectroscopy system is used to render one or more sample properties. 8.The apparatus in claim 4 wherein the X-ray fluorescence spectrometer isportable.
 9. The apparatus in claim 8 wherein a soil water sensor isplaced proximal to the sample analysis area and is communicably coupledwith the onboard computer.
 10. The apparatus in claim 4 wherein theframe is modularly linkable with at least one of the X-ray fluorescencespectrometer, electric field sensor, and X-ray radiation detector. 11.The apparatus in claim 4 wherein a radiation source is placed proximalto the sample and is communicably coupled with and activated by the oneor more processors.
 12. The apparatus in claim 4 wherein the distancebetween the sample and at least one of the X-ray fluorescencespectrometer, electric field sensor, and X-ray radiation detector arecontrolled by placing the samples into a cavity or groove.
 13. Theapparatus in claim 4 wherein the one or more processors are communicablycoupled to a mobile phone or tablet.
 14. The apparatus in claim 13wherein data is transmitted from the apparatus to the mobile phone andto a network connected nontransitory computer database.
 15. Theapparatus in claim 14 wherein the mobile phone or tablet furthertransfer one or more of the associated sample properties wirelessly to ato a non-transitory computer readable medium such as an NFC sticker. 16.The apparatus in claim 4 wherein the data from one or more of the X-rayfluorescence spectrometer, electric field sensor, and X-ray radiationdetector are used in at least one of RL-PLSR, PLSR, SLR, or multivariatetechniques.
 17. A method for determining one or more soil propertiescomprising the steps of: a pilot hole is created in a soil profile byinserting and removing a rigid structure possessing a cavity from thesoil profile; at least one of an electric field sensor and an X-rayradiation detector is inserted into the pilot hole; an X-rayfluorescence instrument is placed proximal to the pilot hole; the soilproximal to the pilot hole is scanned using the X-ray fluorescenceinstrument and at least one of the electric field sensor and the X-rayradiation detector; and the one or more soil properties is determinedbased on at least one of an electric field of the soil indicated by theelectric field sensor and X-Ray radiation traversing the sampleindicated by the X-ray radiation detector, at least one property of theelectric field being altered by sample irradiation via the X-rayfluorescence instrument, the one or more soil properties including atleast one of physical or chemical composition of the soil.
 18. Themethod in claim 17 wherein at least one of the electric field sensor andX-ray radiation detector is partially or wholly covered in a protectivebarrier.
 19. A method for preparing and analyzing a sample comprisingthe steps of: at least one of compactional or vibrational energy isapplied to the sample thereby reducing sample volume; the sample isscanned using an X-ray fluorescence spectrometer and at least one of anelectric field sensor and an X-ray radiation detector, at least oneproperty of the electric field being altered by sample irradiation viathe X-ray fluorescence spectrometer; and one or more sample propertiesare determined based on scan data generated by the X-ray fluorescencespectrometer and at least one of the electric field sensor and the X-Rayradiation detector, the sample properties including at least one ofphysical or chemical composition of the sample.
 20. A method fordetermining sample information comprising the steps of: irradiating asample via a radiation source; scanning the sample via an electric fieldsensor during or after the irradiation to determine an electric field ofthe sample, at least one property of the electric field being altered bysample irradiation via the radiation source; determining one or moresample properties using an onboard computer coupled to the electricfield sensor and the radiation source, based on the electric field ofthe sample, the one or more sample properties including at least one ofphysical or chemical composition of the sample.